Project

MMPE

Multi-modal and Language Technology Based Post-Editing Support for Machine Translation

Multi-modal and Language Technology Based Post-Editing Support for Machine Translation

  • Duration:
  • Application fields
    Other

In order to ensure professional human quality level translation results, in many cases, the output of Machine Translation (MT) systems has to be manually post-edited by human experts. The postediting process is carried out within a post-editing (PE) environment, a user-interface, which supports the capture and correction of mistakes, as well as the selection, manipulation, adaptation and recombination of good segments. PE is a complex and challenging task involving considerable cognitive load. To date, PE environments mostly rely on traditional graphical user interfaces (GUIs), involving a computer screen as display and keyboard and mouse as input devices. In this research project we propose the design, development, implementation and extensive road-testing and evaluation of a novel multi-modal post-editing support for machine translation for translation professionals, which extends traditional input techniques of a PE system, such as keyboard and mouse, with novel free-hand and screen gestures, as well as speech and gaze input modalities (and their combinations). The objectives of the research are to increase the usability and the user experience of post-editing Machine Translation and to reduce the overall cognitive load of the translation task, supporting (i) the core post-editing tasks as well as (ii) controlling the PE system and environment. The multimodal PE environments will be integrated with quality prediction (QE) to automatically guide search for useful segments and mistakes, as well as automatic PE via incremental adaptation of MT to PEs to avoid repeat mistakes, in order to achieve the above mentioned objectives. The environments will be road-tested with human translation professionals and trainees and (where possible) within the partner projects in the Paketantrag (Riezler, Frazer, Ney and Waibel) and (where possible) the post-edited data captured will feed into dynamic and incremental MT retraining and update approaches pursued in the partner projects.

Sponsors

Deutsche Forschungsgemeinschaft (DFG)

GE 2819/2-1

Deutsche Forschungsgemeinschaft (DFG)

Publications about the project

Rashad Albo Jamara, Nico Herbig, Antonio Krüger, Josef van Genabith

In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics. Annual Meeting of the Association for Computational Linguistics (ACL-2021) August 1-6 Bangkok/Virtual Thailand 2021.

To the publication
Nico Herbig, Antonio Krüger, Josef van Genabith

In: Tra&Co Group (editor). Translation, Interpreting, Cognition: The Way Out of the Box. Chapter 1 Pages 1-32 Language Science Press 2021.

To the publication
Nico Herbig, Antonio Krüger, Josef van Genabith

In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing (EMNLP-2021) November 7-11 Punta Cana Dominican Republic Pages 10173-10185 Association for Computational Linguistics 2021.

To the publication

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz